Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis
EJ van Kempen, M Post, M Mannil, RL Witkam… - European …, 2021 - Springer
Objectives Different machine learning algorithms (MLAs) for automated segmentation of
gliomas have been reported in the literature. Automated segmentation of different tumor …
gliomas have been reported in the literature. Automated segmentation of different tumor …
Wavelet integrated CNNs for noise-robust image classification
Abstract Convolutional Neural Networks (CNNs) are generally prone to noise interruptions,
ie, small image noise can cause drastic changes in the output. To suppress the noise effect …
ie, small image noise can cause drastic changes in the output. To suppress the noise effect …
WaveCNet: Wavelet integrated CNNs to suppress aliasing effect for noise-robust image classification
Though widely used in image classification, convolutional neural networks (CNNs) are
prone to noise interruptions, ie the CNN output can be drastically changed by small image …
prone to noise interruptions, ie the CNN output can be drastically changed by small image …
Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis …
Purpose Deep learning (DL) has led to widespread changes in automated segmentation
and classification for medical purposes. This study is an attempt to use statistical methods to …
and classification for medical purposes. This study is an attempt to use statistical methods to …
EEG-based emotion recognition with deep convolutional neural networks
The emotional state of people plays a key role in physiological and behavioral human
interaction. Emotional state analysis entails many fields such as neuroscience, cognitive …
interaction. Emotional state analysis entails many fields such as neuroscience, cognitive …
[HTML][HTML] Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture
Histopathologic diagnosis relies on simultaneous integration of information from a broad
range of scales, ranging from nuclear aberrations (≈ O (0.1 μ m)) through cellular structures …
range of scales, ranging from nuclear aberrations (≈ O (0.1 μ m)) through cellular structures …
Research on improved wavelet convolutional wavelet neural networks
JW Liu, FL Zuo, YX Guo, TY Li, JM Chen - Applied Intelligence, 2021 - Springer
Convolutional neural network (CNN) is recognized as state of the art of deep learning
algorithm, which has a good ability on the image classification and recognition. The …
algorithm, which has a good ability on the image classification and recognition. The …
Tumor diagnosis against other brain diseases using T2 MRI brain images and CNN binary classifier and DWT
TN Papadomanolakis, ES Sergaki, AA Polydorou… - Brain Sciences, 2023 - mdpi.com
Purpose: Brain tumors are diagnosed and classified manually and noninvasively by
radiologists using Magnetic Resonance Imaging (MRI) data. The risk of misdiagnosis may …
radiologists using Magnetic Resonance Imaging (MRI) data. The risk of misdiagnosis may …
Multi-scale wavelet network algorithm for pediatric echocardiographic segmentation via hierarchical feature guided fusion
The automatic segmentation of critical anatomical structures in pediatric echocardiography
is the essential steps for early diagnosis and treatment of congenital heart disease …
is the essential steps for early diagnosis and treatment of congenital heart disease …